A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

Abstract Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging‐based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19–89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within‐network connections (especially default mode and ventral attention networks) and increase between‐network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.

. Distribution of the fraction of explained variance (R 2 ) across 200 repetitions of cross-validation for age and each of the eight cognitive metrics.  shown by the same sequence as in Figure 3C. Subplot A shows results for age-predictive models, and subplot B shows results for gF-predictive models.  Figure S3. Similarity of within-network weight maps between age-and cognition-predictive models. To examine whether within-network weight maps between age-and cognitionpredictive models show higher similarities than randomly selected connections, we conducted a bootstrap test. Specifically, for each functional network we randomly selected 200 withinnetwork connections without replacement 1000 times; and then calculated the correlation of weight maps from age-predictive and cognition-predictive models for each iteration. Further, we randomly selected 200 connections from the whole connectome 1000 times and calculated correlations of weight maps from age-predictive and cognition-predictive models for each iteration. Differences between the within-network weight maps and randomly selected weight maps were compared using a two-sample t-test. Overall, among all eight networks, only DAN and LIM have lower similarity in weight patterns than a matched number of randomly selected connections. For the VSTM-predictive model, DAN and VIS have lower similarity in weight patterns than a matched number of randomly selected connections.

Figure S6.
Predictive results based on connectome-based predictive modeling (CPM) [1][2][3][4][5] , CPM works by (i) calculating the correlation of each connection to the target measure (e.g., age) across training subjects, and retaining the most significantly correlated ones under a predefined threshold; (ii) separating the selected features into a positive tail (the positively-correlated connections) and a negative tail (the negatively-correlated connections); (iii) separately summing the selected connections in the positive and negative tails into a single aggregate metric (positive network strength, negative network strength); (iv) submitting the aggregate metrics to a linear regression model. Detailed implementation can be found in [1,2] . Overall, results showed that the CPM method achieved slightly lower prediction accuracy than PLSR, but the identified predictive patterns were highly similar to those revealed by PLSR. Figure S7. Prediction accuracies based on within-or between-network connections. To examine which functional network contributes more to the prediction than others, we reran the prediction framework using only within-network or between-network connections to predict age. All network pairs achieved a prediction accuracy lower than models based on whole-brain features. However, we found that networks having more connections are more likely to better predict age. Nevertheless, there are some interesting findings. For example, there are only a medium number of connections within DMN. But it achieved a relative higher accuracy in predicting age than its size-matched counterparts.  3a or Session 3b [6] . Cognitive measures of force matching and motor learning only appeared in either Session 3a or Session 3b. Therefore, only half of the participants have available data for these two cognitive tasks. Description for each of the eight behavioral tasks were directly copied from [7] , while more details can be found in [6] .

MRI data acquisition
Cam-CAN: Details of fMRI data acquisition can be found in [6,7] . Briefly, resting-state scans were collected while participants rested with their eyes closed. In the movie-watching task, participants were scanned while they watched an excerpt of a compelling but unfamiliar film: "Bang! You're Dead", which is condensed from its original time of about 30 min to 8 min with the essential plot preserved. In the sensorimotor task, participants respond to 129 trials consisting of an initial practice trial, 120 bimodal audio/visual trials, and eight unimodal trials included to discourage strategic responding to one modality.
Imaging data were acquired using a 3T Siemens TIM Trio scanner with a 32-channel head

Preprocessing
The DiffusionKit (diffusion.brainnetome.org) and in-house code were used for fMRI preprocessing, following the general framework in aging studies [8,9] . We applied similar preprocessing strategy to all three datasets, which was the same as our previous publications.
The BOLD echo planar image data for all three states were unwrapped based on field-map images to compensate for magnetic field inhomogeneities, realigned to correct motion effects where the motion parameters for each volume image were stored for the following regression, and slice-time corrected. The first 10 volumes were discarded to allow for magnetic equilibration and then nonlinearly registered to MNI 3-mm space (for validation datasets, we did not discard any volumes because they only included a small number of volumes). We further scrubbed the frames with excessive head motions based on framewise displacement (FD) >0.5 mm criterion and corrected the frames by interpolation. We discarded images with less than 40% of their original data after scrubbing. Moreover, fMRI scans with a mean FD>0.3 mm were excluded from further analysis. We then band-pass filtered the data at 0.009-0.08 Hz to reduce low-frequency drift and high-frequency noise. CompCor was used to reduce physiological effects as performed in [10,11] . Specifically, the mean signal and 5 principal components of white matter and cerebrospinal fluid and movement parameters and their derivatives were regressed out as confounding factors to remove physiological noise. The aforementioned principal components were derived separately by decomposing the regional signal masked by the eroded white matter and cerebrospinal fluid. In light of the fact that the location of functional regions was more variable in older adults, which can be alleviated by smoothing [11] , we smoothed the volume images by a Gaussian filter with a kernel size of 6 mm. Considering a controversial physiological interpretation, global signal regression was not performed here. As previous studies confirmed the advantages of longer scan length, we concatenated fMRI time series from all three fMRI conditions [12][13][14] . Time courses from the task fMRI were calculated based on the raw task fMRI data, with no regression of task-evoked activity [15] , resulting in a total length of 685 time points for Cam-CAN data. For validation cohorts, only resting-state fMRI was available, therefore, the total length of time points did not change.  A21r  MTG_L_4_2 7  -53, 2, -30 206 iOccG  LOcC_R_4_4 1  32, -85, -12  84  A21r  MTG_R_4_2 7  51, 6, -32 207 msOccG LOcC_L_2_1  1  -11, -88, 31  85  A37dl  MTG_L_4_3 3  -59, -58, 4 208 msOccG LOcC_R_2_1 1  16, -85, 34  86  A37dl  MTG_R_4_3 3  60, -53, 3 209 lsOccG  LOcC_L_2_2  1  -22, -77, 36  87  aSTS  MTG_L_4_4 7  -58, -20, -9 210 lsOccG  LOcC_R_2_2 1  29, -75, 36  88  aSTS  MTG_R_4_4 7